Diffusion-weighted magnetic resonance imaging (DWI) is undergoing constant evolution with the ambitious goal of developing in-vivo histology of the brain. A recent methodological advancement is Neurite Orientation Dispersion and Density Imaging (NODDI), a histologically validated multi-compartment model to yield microstructural features of brain tissue such as geometric complexity and neurite packing density, which are especially useful in imaging the white matter. Since NODDI is increasingly popular in clinical research and fields such as developmental neuroscience and neuroplasticity, it is of vast importance to characterize its reproducibility (or reliability). We acquired multi-shell DWI data in 29 healthy young subjects twice over a rescan interval of 4 weeks to assess the within-subject coefficient of variation (CV_WS), between-subject coefficient of variation (CV_BS) and the intraclass correlation coefficient (ICC), respectively. Using these metrics, we compared regional and voxel-by-voxel reproducibility of the most common image analysis approaches (tract-based spatial statistics [TBSS], voxel-based analysis with different extents of smoothing [“VBM-style”], ROI-based analysis). We observed high test–retest reproducibility for the orientation dispersion index (ODI) and slightly worse results for the neurite density index (NDI). Our findings also suggest that the choice of analysis approach might have significant consequences for the results of a study. Collectively, the voxel-based approach with Gaussian smoothing kernels of ≥4 mm FWHM and ROI-averaging yielded the highest reproducibility across NDI and ODI maps (CV_WS mostly ≤3%, ICC mostly ≥0.8), respectively, whilst smaller kernels and TBSS performed consistently worse. Furthermore, we demonstrate that image quality (signal-to-noise ratio [SNR]) is an important determinant of NODDI metric reproducibility. We discuss the implications of these results for longitudinal and cross-sectional research designs commonly employed in the neuroimaging field.

扩散加权磁共振成像（DWI）正在不断发展，其雄心勃勃的目标是发展大脑的体内组织学。最近的方法学进展是神经突取向分散和密度成像（NODDI），这是一种经过组织学验证的多室模型，可产生大脑组织的微结构特征，例如几何复杂度和神经突堆积密度，这在成像白质时特别有用。由于NODDI在临床研究和发育神经科学和神经可塑性等领域中越来越受欢迎，因此表征其可再现性（或可靠性）非常重要。我们在4周的重新扫描间隔中两次获取了29位健康年轻受试者的多壳体DWI数据，以评估受试者内部变异系数（CV _WS），受试者之间变异系数（CV _BS））和组内相关系数（ICC）。使用这些指标，我们比较了最常见的图像分析方法（基于区域的空间统计数据[TBSS]，具有不同程度的平滑度的基于体素的分析[“ VBM样式”]，ROI-基础分析）。我们观察到定向分散指数（ODI）的重测重现性高，而神经突密度指数（NDI）的结果稍差。我们的发现还表明，分析方法的选择可能会对研究结果产生重大影响。总的来说，基于体素的方法具有≥4 mm FWHM的高斯平滑核和ROI平均，在NDI和ODI贴图（CV _WS大部分≤3％，ICC大多≥0.8），而较小的内核和TBSS始终表现较差。此外，我们证明图像质量（信噪比[SNR]）是NODDI度量可重复性的重要决定因素。我们讨论了这些结果对神经成像领域常用的纵向和横截面研究设计的影响。